Enhancing the IT service desk function through unobtrusive user profiling, personalization and stereotyping

HP Software University Association (HPSUA) 14th Workshop Garching/Munich, Germany

8th – 11th July 2007

Enhancing the IT service desk function

through unobtrusive user profiling,

personalization and stereotyping

A.Zaslavsky 1_{, C.Bartolini} 2_{, A. Boulmakoul} 3_{, O.Alahakoon} 1_{, S.W.Loke} 4_{, F.Burstein} 1 1 _{Caulfield School of IT, Monash University, Australia}

{arkady.zaslavsky, oshadi.alahakoon, frada.burstein}@infotech.monash.edu.au

2 _{HP Labs, Palo Alto, USA, claudio.bartolini@hp.com} 3 _{HP Labs, Bristol, UK, abdel.boulmakoul@hp.com}

Outline

•Introduction and motivation

•eHermes - Personalization and profiling in e-commerce applications

•Integrating IT service management and eHermes personalization approach

•Related work and comparison •Conclusions and future work

User modeling background

•User modeling:

-construction of (typically computer-based) models of

users' mental activities and behaviors, often used to make predictions about a system's usability or as a basis for

interactive help systems (ei.cs.vt.edu/~cs5714/glossary.html)

-User modeling (UM) aims to make information systems really user-friendly, by adapting the behavior of the system to the needs of the individual

(http://www.ionio.gr/~papatheodor/papers/Papatheodorou_formatted.pdf)

•User personalization:

-Personalization (or personalisation) is tailoring a

consumer product, electronic or written medium to a user based on personal details or characteristics they provide.

User models

•User profiling can also be defined as a process of

identifying characteristics which are closely associated with a particular user or a target group of users, in other words, roles. It aims to identify either characteristics of individual users, or individuals who have the same or

Motivating scenario

•A typical interaction of a customer with the service desk begins with a service call.

•A call is any communication by a customer with the service desk, regardless of the method of

communication (telephone, e-mail, voice-mail, and so on).

•Incidents are events, which are not part of the standard operation of a service, and could cause an interruption to or a reduction in the quality of service (QoS).

Motivating scenario (cont’d)

•A service request is a request for new or altered service. •The types of service requests vary between

organizations, but common ones include requests for change (RFCs), requests for information (RFIs), and

service extensions.

•Service calls are handled by the initial support team,

which is the team providing the very first line of support for processing incidents and service requests.

Personalization in IT service desk management

•Hasn’t received proper deserved attention •Lack of adaptable, flexible interface

•Lack of tools

•Need for unobtrusive profiling in order to increase QoS, efficiency, effectiveness and user-friendliness

eHermes - Personalization and profiling in e-commerce

™

Need of personalization to attract consumers to

ecommerce websites.

™

Personalized but less obtrusive interactions

with consumers.

• To minimize user interactions by asking fewer number of

questions when searching for product needs

™

Understanding users irrespective of the domain

™

Single user model that can provide

eHermes approach

Main components of the system

There are 3 main components

1. The layered user model

2. Question graph – interactive, personalised

question selection process, using the layered user model

3. Domain hierarchies to maintain inter connection

User Model

Layer 2

Layer 3 Layer 1

User model, question graph and domain hierarchies

Question Graph Q₃ Q₅ Q₈ Q₇ Q₆ Q₂ Q₄ Q₁

User needs personalised interaction in a selected

domain

The Layered User model

™Considering the different user information required for

behavior predictions, the single user profile we propose consists of three information layers.

™L1 – Personal data/Demographics + behavior

characteristics

™L2 – Domain specific user behavior information

™L3 – Product descriptive data collected during each

transaction

™These layers make up the user profile which can be

considered as the ‘heart’ of the proposed system.

™The profile stores and maintains information necessary

for supporting all interactions and services to the registered users.

Prototype system

Form to enter initial registration information

<?xml version="1.0" encoding="utf <?xml version="1.0" encoding="utf--8" ?>8" ?> <ProfileL1 <ProfileL1 xmlns="http://tempuri.org/XMLSchema1.xsd"> xmlns="http://tempuri.org/XMLSchema1.xsd"> <PersonalInfo>

<PersonalInfo> </PersonalInfo></PersonalInfo> <Characteristics></Characteristics>

<Characteristics></Characteristics>

</ProfileL1>

</ProfileL1> Prototype system

Prototype system –– First layer (L1) of the user modelFirst layer (L1) of the user model This layer holds

This layer holds

™

™UserUser’’s personal information collected using the previous s personal information collected using the previous

form

form

™

™User characteristics calculated based on above User characteristics calculated based on above

information

Example of personal information stored

Example of calculated characteristic values - based

on above information

Layer 2 of the Model (L2)

Domain Features

d

f

A given domain

d

domain features d

f

Domain features

Domain features

are collections of

are collections of

attributes

attributes

™Initially userInitially user’’s preferred s preferred

attributes

attributes

the values calculated for each behavioral characteristic.

the values calculated for each behavioral characteristic.

™

™One or more characteristics are relevant to each One or more characteristics are relevant to each

attribute

attribute

™Example of a heuristic:Example of a heuristic:

A less price sensitive person will have a

A less price sensitive person will have a higher chance higher chance

of accepting a high cost restaurant

Layer 2 holds user

Layer 2 holds user

’

’

s domain dependent behaviour

s domain dependent behaviour

™User’s preferred attributes selected based on

characteristics values

™A relevance value indicating the relevance of a given

attribute to the user

™A confidence value associated with each feature

™Indicate the system confidence of that value,

depending reliability of the source acquired.

™These attribute values are used (combined with their

relevance and confidence) to filter the user’s preferred products

Format of the details stored in the layer two

Format of the details stored in the layer two

<?xml version="1.0" encoding="utf-8" ?> <ProfileL2 xmlns="http://tempuri.org/ProfileL2.xsd"> <Identification> <UserId></UserId> <DomainId></DomainId> </Identification> <Preferences> <Feature></Feature> <Attribute></Attribute> <Relevance></Relevance> <Confidence></Confidence> </Preferences>

Example details stored in the layer two

Level 3 of the Model (L3)

¾This layer consists of the transaction information during

each user session.

¾These are derived from the answers user provided

regarding product attributes.

¾This information is represented as attribute value pairs.

¾Each domain feature is described using a set of attribute

value pairs.

Product Attributes and Values

a

,v

>

Information generalisation in layer 3 to layer 2

Information generalisation in layer 3 to layer 2

•Transaction information in layer 3 is generalised to form values for layer 2.

•Such generalisation mechanisms depend on the nature of the attribute values (symbolic attributes, numeric attributes etc.)

•With time, when the number of transactions increases,

accuracy in 2nd _{layer feature values become high}

•Changing nature of user needs are taken in to account by making the user provide an initial query each time user visit the system.

Example user model of a user interacted eight

times in four different domains

L1

d₁L2 _{d2L2 d}

3L2 d4L2

d₁T₁ d₁T₂ d₁T₃ d₂T₁ d₃T₁ d₃T₂ d4T1 d4T2

Level 1

Level 1 -- User’User’s personal s personal information

information

Level 2

Level 2 --Domain Domain dependent user dependent user

descriptions descriptions

Question Graph for Intelligent User Interaction

A CBR (Case Based Reasoning) approach is employed to question selection.

•The user interaction can be represented as a graph, where nodes contain questions.

•Each of these question nodes are dialog situations.

• An edge is an answer leading to another question /dialog situation (another node).

•To isolate the group of items required by the user, product attributes need to be constrained.

•To select the ideal product it is required to ask values for all the attributes from the user.

Q

Q

Q

Q

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

Next question is determined by the answer given

Next question is determined by the answer given

to the previous question

Unobtrusiveness

To achieve unobtrusiveness the number of

questions directed to the user are need to be

reduced. This is done as follows

•Obtaining an initial query from the user with his/her most important attribute restrictions.

•Using his/her preferred attribute values in the second layer of the user model

•Once constraints are formed using above two steps, ask the minimum number of questions to whittle down the possible items

Algorithm

Algorithm

values to avoid constraining similar items

Form a query for a parametric search

If (No_Of_Items

> 3)

Retrieve items from the database

Calculate entropy to find first (%5) of the most discriminating attributes, for the selected items

(Dis_Attri_Count) = N

Check L2 of the user model for selected

attribute_value relevance

If (attribute_value is found && relevance =>2) N = N-1

Pick the next most discriminating attribute If N >0 Count No_Of_Items No Yes Yes No Yes No Terminate search and move to presenting items User selects attribute

values as initial selection criteria

Product taxonomies and Domain Hierarchies

•All the domains usable with the user model are arranged in a hierarchy.

•When a new domain joins in, it is inserted to the correct position within the domain hierarchy.

•Domains are described using domain features.

•Domains down the hierarchy inherit the domain features of domains up hierarchy.

•Domain features are described by attributes.

•In product taxonomies, products are described using attributes.

Product taxonomies and Domain Hierarchies

™Each time a user selects a new domain, a new L2 layer is

created.

™The new L2 hold the domain features/ user needs in the

given domain.

™Depending on its position in the domain hierarchy some

of the features are inherited from the upper layers of the hierarchy.

™Although there are three information layers in the user

Multi-Agent architecture

¾Since the profile building is meant to be used online, a

multi-agent system is proposed for efficient and parallel handling of operations during the interactions.

¾ Each component in the system is handled by a

dedicated agent.

¾ Employment of agents, also facilitate easy conversion to

distributed environments where vendor sites are scattered over different servers.

¾In such environments the system could be upgraded by

Interactions

A. User interaction with the Interface Agent (eg. Product information gathering).

B. Interface Agent communicates with the Profile Instance Agent for information in the current profile for specific domain for the individual.

C. Demographic based characteristics of user buying behavior are passed on to the L2 Agent by filtering and tailoring to the specific domain, at the creation of L2 layer of the profile. Corresponding features are found using the mappings in domain descriptions data.

D. Domain specific user information in L2 is passed on to the Profile Instance Agent, who has to communicate with the Interaction Agent during the transaction.

E. This happens after and when several consumer behavioral trends have become visible across domains.

F. Individual transaction instances are generalized to layer 2 of the profile.

G. Answers to the question either obtained from the Profile Instance Agent or from the user, is prepared to be recorded in L3 layer of the profile.

H. Above prepared answers are stored as a new transaction (L3).

I. L2 Agent populates the L2 layer of the profile by obtaining the domain

description data when a new domain is selected for the first time.

Various agents and services they provide

1. L1 Agent – L1 Agent generate the user’s common personality traits in human buying behavior (eg. Time saver, Price sensitivity etc).

2. L2 Agent – The L2 Agent is responsible of generating and maintaining of the second layer information. Initial values are generated acquiring information from the L1 layer and then maintained by generalized L3 transaction information.

3. L3 Agent – Maintains individual transaction instances, under separate domains.

4. Profile Instance Agent - The Profile Instance Agent is activated for each transaction to represent the current ‘image’ of the individual purchasing behavior and preference, in the particular domain.

5. Interface Agent – The Interface Agent manages the interaction between the user and the profile instance.

6. Transaction Processing Agent – The Transaction Processing Agent gathers user answers regarding product attributes, mapping them and uploading the L3 instance of the particular transaction.

Privacy Issues

•Since the user model stores user personal data it is necessary to ensure security of the information.

•The layered architecture supports locating each layer in different locations if preferred.

•For example storing the layer 1 (user demographics) with the user (with appropriate encryption) will make user feel safer rather than storing it in a server.

•The user model architecture supports a server based implementation

•Since users are reluctant to provide their personal information: as required in the layer 1 (L1), an alternative of directly providing values for characteristics are allowed.

Sources of user information

Related Work

•Most systems providing personalisation place the user model within the application

•User modelling shell systems developed for generic user modelling tasks did not have the ability to reuse collected user information in different applications.

•Server based Doppelganger user modelling system introduced the idea of a single user model.

•Ms passport, liberty alliance are simple data stores which maintain user information (such as demographics) but do not have inferencing abilities.

•Later developed user modelling servers such as Personis reuse once acquired user information for more than single application

Related work in each of the main components of the work

¾The layered user model – is related to work on ‘Personis’

user modelling server.

¾The user interface – related to entropy and similarity

measured question selection strategies used in CBR systems.

¾The knowledge engineering and knowledge base is

related to Entrée system and the same data set used for Entrée is used in prototype building.

¾The multi agent architecture and product hierarchies are

related to the multi agent systems and general ontology used in SETA and PPG systems